In today's wireless 4G LTE networks, the spectral allocation of network resources is independent of the Quality of Service (QoS) requirements of the specific application and/or independent of the users' specific perceived QoS, or at most, relies on a set of pre-defined fixed priorities. Although in these standards, the media access control (MAC) and physical (PHY) layers have an increased role in optimizing the usage of the spectral resources and implementing link quality-aware techniques, optimization is still largely independent of the application content, the users' requirements and the user's perception of performance degradation. The allocation of resources does not take into account the QoS required by different applications and their users, beyond simply assigning fixed priorities to traffic classes. Indeed, from the user's perspective, the QoS required by different applications can be quite variable. Similarly, for a given application type, different users may require different levels of QoS.
For example, in Voice over IP (VoIP) applications, the perceived voice quality of different languages may differ substantially when allocated the same data rate and Bit Error Rate (BER), as a result of the different spectral content of the various languages and because of a particular user's auditory spectral response (with variations typically due to aging), making the user more or less sensitive to a particular type of distortion. Consequently, the same amount of degradation, as experienced by specific applications and the users of the applications, may have substantially different perceptual effects. Various conversation environments may also require a different quality of service for different users, wherein some users may be having a conversation under very noisy conditions, while other users may be conversing under very quiet conditions, thus making users more or less sensitive to packet losses depending upon the conversation environment. If the same spectral resources are allocated to users in very noisy conditions and very quiet conditions, then very different user experiences will likely be observed. As another example, considering that people from different age groups normally have varying sensitivity to high frequency content, this variability can be exploited to maximize the system capacity by reducing the bit rate for users with reduced frequency sensitivity.
For video applications, as a user-specific QoS example, older individuals are less sensitive to spatial form defined by temporal structure, as compared to younger adults. So, for many older people, a lower video data rate provides the same user experience as the full rate video does for younger people. As another user-specific QoS example, to achieve the same user experience for different video content (e.g. news and sports video), the required video data rate can be quite different. The required data rate of news video can be much less than that of sports video.
Accordingly, there is a need in the art for a system and method that utilizes the user-specific QoS requirements and a scheduler to differentiate the users and to make better use of the wireless spectral resources, thereby maximizing spectrum utilization while maintaining user satisfaction.